Miscellaneous

A little over two years ago, I wrote an article called Male Chauvinist Machines. At the time, men outnumbered women in artificial intelligence development roles by about eight to one. A more recent report suggests the ratio is now about three to one.

The problem is not just that men outnumber women. Data mining also presents an issue. If machines mine data from the past (what other data is there?), they may well learn to mimic biases from the past. Amazon, for instance, recently found that its AI recruiting system was biased against women. The system mined data from previous hires and learned that resumés with the word “woman” or “women” were less likely to be selected. Assuming that this was the “correct” decision, the system replicated it.

Might men create artificial intelligence systems that encode and perpetuate male chauvinism? It’s possible. It’s also possible that the emergence of AI will mean the “end of men” in high skill, cognitively demanding jobs.

The paper documents a shift in hiring in the United States since 1980. During that time the probability that a college-educated man would be employed in a

“… cognitive/high wage occupation has fallen. This contrasts starkly with the experience for college-educated women: their probability of working in these occupations rose.”

The shift is not because all the newly created high salary, cognitively demanding jobs are in traditionally female industries. Rather, the shift is “….accounted for by a disproportionate increase in the female share of employment in essentially all good jobs.” There seems to be a pronounced female bias in hiring for cognitive/high wage positions — also known as “good jobs”.

Why would that be? The researchers consider that “…women have a comparative advantage in tasks requiring social and interpersonal skills….” So, if industry is hiring more women into cognitive/high-wage jobs, it may indicate that such jobs are increasingly requiring social skills, not solely technical skills. The researchers specifically state that:

“… our hypothesis is that the importance of social skills has become greater within high-wage/cognitive occupations relative to other occupations and that this … increase[s] the demand for women relative to men in good jobs.”

The authors then present 61 pages on hiring trends, shifting skills, job content requirements, and so on. Let’s just assume for a moment that the authors are correct – that there is indeed a fundamental shift in the good jobs market and an increasing demand for social and interpersonal skills. What does that bode for the future?

We might want to differentiate here between “hard skills” and “soft skills” – the difference, say, between physics and sociology. The job market perceives men to be better at hard skills and women to be better at soft skills. Whether these differences are real or merely perceived is a worthy debate – but the impact on industry hiring patterns is hard to miss.

How will artificial intelligence affect the content of high-wage/cognitive occupations? It’s a fair bet that AI systems will displace hard skills long before they touch soft skills. AI can consume data and detect patterns far more skillfully than humans can. Any process that is algorithmic – including disease diagnosis – is subject to AI displacement. On the other hand, AI is not so good at empathy and emotional support.

If AI is better at hard skills than soft skills, then it will disproportionately displace men in good jobs. Women, by comparison, should find increased demand (proportionately and absolutely) for their skills. This doesn’t prove that the future is female. But the future of good jobs may be.

Daniel Kahneman, the psychologist who won the Nobel prize in economics, reminds us that, “What you see is not all there is.” I thought about Kahneman when I saw the videos and coverage of the teenagers wearing MAGA hats surrounding, and apparently mocking, a Native American activist who was singing a tribal song during a march in Washington, D.C.

The media coverage essentially came in two waves. The first wave concluded that the teenagers were mocking, harassing, and threatening the activist. Here are some headlines from the first wave:

The Stranger: “I Thought the MAGA Boys Were S**t-Eating Monsters. Then I Watched the Full Video.”

So, who is right and who is wrong? I’m not sure that we can draw any certain conclusions. I certainly do have some opinions but they are all based on very short video clips that are taken out of context.

What lessons can we draw from this? Here are a few:

Reality is complicated and — even in the best circumstances — we only see a piece of it.

We see what we expect to see. Tell me how you voted, and I can guess what you saw.

It’s very hard to draw firm conclusions from a brief slice-of-time sources such as a photograph or a video clip. The Atlanticmagazine has an in-depth story about how this story evolved. One key sentence: “Photos by definition capture instants of time, and remove them from the surrounding flow.”

There’s an old saying that “Journalism is the first draft of history”. Photojournalism is probably the first draft of the first draft. It’s often useful to wait to see how the story evolves. Slowing down a decision process usually results in a better decision.

It’s hard to read motives from a picture.

Remember that what we see is not all there is. As the Heath brothers write in their book, Decisive, move your spotlight around to avoid narrow framing.

Humans don’t like to be uncertain. We like to connect the dots and explain things even when we don’t have all the facts. But, sometimes, uncertainty is the best we can hope for. When you’re uncertain, remember the lessons of Appreciative Inquiry and don’t default to the negative.

Alabama and Clemson have met each year for the past four years in the college football playoffs. Alabama has won two games; Clemson has won two. The aggregate score of the four games: Clemson 121 — Alabama 120. If Alabama hadn’t missed an extra point in last night’s game, the aggregate score would be tied. The two teams are so close that they might as well be one. Let’s call them Clembama.

Meanwhile, no other team has come close. The great teams of years past – Notre Dame, Oklahoma, Georgia, Southern Cal, Nebraska, and Texas – have all fallen by the wayside. When they match up against Clemson or Alabama, they don’t lose by inches. They lose by yards.

What’s it all mean? Simply that skill is unevenly distributed in college football. As Michael Mauboussin points out, when skill is evenly distributed, luck plays a greater role in the outcome of any competitive event, including sports and business competition. When skill is unevenly distributed, luck’s role is greatly diminished.

It seems counter-intuitive that luck should be more important in some situations than in others. Isn’t luck more or less random? Shouldn’t it apply equally in all situations? It’s true that luck is essentially random but when everything else is even, even a little bit of luck can make a huge difference. A funny bounce, an odd hop, a slippery field can determine who wins and who loses.

To see the difference, just look at the NFL, where skill is more evenly distributed. More specifically, look at Sunday’s game between the Chicago Bears and the Philadelphia Eagles. The Eagles were ahead by one point when the Bears maneuvered into position to kick a field goal near the end of the game. Make the field goal and the Bears win. Miss it and the Eagles win. The Bears kicked, the ball hit an upright, bounced downward, hit the crossbar, and then bounced back into the field of play. A bouncing football is a pretty random thing. If the ball had bounced off the crossbar and through, the Bears would have won. As it was, the Eagles won. In truth, luck – not skill –determined the outcome.

If Oklahoma, say, had made the same kick the last time they played Alabama, it would not have made a whit of difference. The game wasn’t close. The skill levels weren’t close. Luck didn’t matter.

Mauboussin’s paradox of skill states that: “In activities that involve some luck, the improvement of skill makes luck more important…” The paradox makes me feel somewhat humble. My business career was in the highly competitive computing industry, where skill is very widely distributed. As I look back on both my successes and my failures, I wonder how many were caused by skill (or lack of it) and how many were caused by luck. When I won, maybe it was because I was more skilled. Or maybe I just got lucky.

I first wrote about Clembama two years ago. Click here to find that article, which includes several links to Michael Mauboussin’s work.

If I ask you about the crime rate in your neighborhood, you probably won’t have a clear and precise answer. Instead, you’ll make a guess. What’s the guess based on? Mainly on your memory:

If you can easily remember a violent crime, you’ll guess that the crime rate is high.

If you can’t easily remember such a crime, you’ll guess a much lower rate.

Our estimates, then, are not based on reality but on memory, which of course is often faulty. This is the availability bias. Our probability estimates are biased toward what is readily available to memory.

The broader concept is processing fluency– the ease with which information is processed. In general, people are more likely to judge a statement to be true if it’s easy to process. This is theillusory truth effect– we judge truth based on ease-of-processing rather than objective reality.

It follows that we can manipulate judgment by manipulating processing fluency. Highly fluent information (low cognitive cost) is more likely to be judged true.

We can manipulate processing fluency simply by changing fonts. Information presented in easy-to-read fonts is more likely to be judged true than is information presented in more challenging fonts. (We might surmise that the new Sans Forgeticafont has an important effect on processing fluency).

We can also manipulate processing fluency by repeating information. If we’ve seen or heard the information before, it’s easier to process and more likely to be judged true. This is especially the case when we have no prior knowledge about the information.

But what if we do have prior knowledge? Will we search our memory banks to find it? Or will we evaluate truthfulness based on processing fluency? Does knowledge trump fluency or does fluency trump knowledge?

Knowledge-trumps-fluency is known as the Knowledge-Conditional Model. The opposite is the Fluency-Conditional Model. Until recently, many researchers assumed that people would default to the Knowledge-Conditional Model. If we knew something about the information presented, we would retrieve that knowledge and use it to judge the information’s truthfulness. We wouldn’t judge truthfulness based on fluency unless we had no prior knowledge about the information.

A 2015 study by Lisa Fazioet. al. starts to flip this assumption on its head. The article’s title summarizes the finding: “Knowledge Does Not Protect Against Illusory Truth”. The authors write that, “An abundance of empirical work demonstrates that fluency affects judgments of new information, but how does fluency influence the evaluation of information already stored in memory?”

The findings – based on two experiments with 40 students from Duke University – suggest that fluency trumps knowledge. Quoting from the study:

“Reading a statement like ‘A sari is the name of the short pleated skirt worn by Scots’ increased participants later belief that it was true, even if they could correctly answer the question, ‘What is the name of the short pleated skirt worn by Scots?’” (Emphasis added).

The researchers found similar examples of knowledge neglect– “the failure to appropriately apply stored knowledge” — throughout the study. In other words, just because we know something doesn’t mean that we use our knowledge effectively.

Note that knowledge neglect is similar to the many other cognitive biases that influence our judgment. It’s easy (“cognitively inexpensive”) and often leads us to the correct answer. Just like other biases, however, it can also lead us astray. When it does, we are predictably irrational.

This fall, in addition to my regular academic courses, I’ll teach three one-day seminars designed for managers and executives.

These seminars draw on my academic courses and are repackaged for professionals who want to think more clearly and persuade more effectively. They also provide continuing education credits under the auspices of the University of Denver’s Center for Professional Development.

If you’re guiding your organization into an uncertain future, you’ll find them helpful. Here are the dates and titles along with links to the registration pages.